Valid DAA-C01 Test Practice | Answers DAA-C01 Real Questions

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Build and Troubleshoot Advanced SQL Queries	20–25%	- Query optimization and troubleshooting - Semi-structured data processing - Complex joins, subqueries, window functions
Topic 2: Perform Simple Data Transformations for Analysis	15–20%	- Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion
Topic 3: Perform Descriptive and Diagnostic Analysis	10–15%	- Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis
Topic 4: Prepare and Present Data	10–15%	- Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements
Topic 5: Perform Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
Topic 6: Prepare and Load Data	15–20%	- Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation - File formats: CSV, JSON, Parquet, Avro
Topic 7: Use Built-in Functions and Create UDFs	10–15%	- Scalar, aggregate, table, system functions - User-Defined Functions (UDFs)

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
When evaluating and selecting data for building dashboards, what factors should be considered for ensuring data relevance and usefulness? (Select all that apply)

A. Including all available data for comprehensive visualization
B. Ignoring data complexities for simplicity in visualization
C. Evaluating data based on business requirements
D. Filtering data based on irrelevant attributes

Answer: C,D

Explanation:
To ensure relevant and useful dashboards, data must be evaluated based on business requirements and filtered for irrelevant attributes.

NEW QUESTION # 24
A marketing team wants to understand the impact of their campaigns on website traffic and conversions. You have the following tables in Snowflake: sCAMPAlGN PERFORMANCE: 'CAMPAIGN ONT), (DATE), 'CLICKS' (INT), 'IMPRESSIONS' (INT), 'COST (NUMBER) 'DATE' (DATE), 'PAGE_VIEWS' ONT), (INT) 'CONVERSIONS' 'DATE (DATE), ONT), (NUMBER) Which SQL query and visualization technique would be most suitable for identifying the correlation between campaign spend and website conversions over time, allowing the team to quickly identify campaigns with a high return on investment (ROI)?

A. Three separate pie charts, one showing the percentage of clicks per campaign, one showing the percentage of impressions per campaign, and one showing the percentage of conversions per campaign.
B. A simple bar chart showing total clicks per campaign ID, generated from a query that joins 'CAMPAIGN PERFORMANCE with 'WEBSITE _ TRAFFIC' on the DATE column.
C. A scatter plot visualizing the relationship between 'COST' from 'CAMPAIGN PERFORMANCE and 'CONVERSION COUNT from 'CONVERSIONS', aggregated by 'DATE and calculated ROI, generated from a query using window functions to compute cumulative sums and moving averages.
D. An excel Pivot table from exported data from all these tables which uses the data points required.
E. Aline chart displaying 'COST from 'CAMPAIGN PERFORMANCE, from 'WEBSITE TRAFFIC, and 'CONVERSION COUNT from 'CONVERSIONS' over time ( ' DATE), joined on the 'DATE column, with a calculated ROI metric displayed as a separate line on the same chart. The query uses a common table expression (CTE) to first calculate daily ROI.

Answer: E

Explanation:
Option D is the most effective. A line chart plotting cost, unique visitors, and conversion count over time allows for visual identification of trends and correlations. Displaying ROI on the same chart provides a direct measure of campaign effectiveness. The CTE helps organize the ROI calculation. Option B, while useful, doesn't explicitly show the temporal relationship. Options A and C offer limited insights into ROI. Exporting and using Excel (E) is not scalable or efficient for dynamic analysis.

NEW QUESTION # 25
Consider a Snowflake table 'USER EVENTS' with a 'VARIANT' column named 'event_data' containing JSON objects representing user activity. The JSON structure varies significantly across rows. You need to extract all the distinct event types from this data'. Which of the following Snowflake queries is the most efficient way to achieve this, handling potential null or missing 'event_type' fields gracefully and avoiding errors? Assume the volume of data is very large.

Answer: D

Explanation:
Option C, using , is the most efficient and robust solution. attempts to convert the JSON value to a string and returns NULL if the conversion fails (e.g., if is an object or array, not a string or a value that can be cast to a string). This avoids errors and simplifies the query. Using 'DISTINCT on the result then gives the distinct event types. Options A, B, D and E have the overhead of IS NULL or NVL functions, that make processing slower and inefficeint compared to C. While these options handle nulls, they are more verbose and potentially less performant due to the explicit null checks. Option A will also exclude rows where event_data:event_type is actually NULL, which might be undesirable.

NEW QUESTION # 26
How do constraints, such as primary keys, impact table joins between parent/child tables in Snowflake?

A. Primary keys allow only one-to-one table joins
B. Primary keys ensure data uniqueness for table relationships
C. Constraints limit table join operations to specific data types
D. Constraints restrict data access to authorized user roles

Answer: B

Explanation:
Primary keys enforce data uniqueness, ensuring integrity and maintaining relationships between parent and child tables in Snowflake.

NEW QUESTION # 27
Why is the Parquet format preferred for complex data sets?

A. It randomly alters data for testing
B. It changes data colors for differentiation
C. It has visually appealing data presentation
D. It supports efficient compression and encoding schemes

Answer: D

NEW QUESTION # 28
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